Gray-Related Support Vector Machine Optimization Strategy and Its Implementation in Forecasting Photovoltaic Output Power
Reliable and accurate photovoltaic (PV) output power projection is critical for power grid security, stability, and economic operation. However, because of the indirectness, unpredictability, and solar energy volatility, predicting precise and reliable photovoltaic output power is a complicated subj...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/3625541 |
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| _version_ | 1832550437275828224 |
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| author | Bo Xiao Hai Zhu Sujun Zhang Zi OuYang Tandong Wang Saeed Sarvazizi |
| author_facet | Bo Xiao Hai Zhu Sujun Zhang Zi OuYang Tandong Wang Saeed Sarvazizi |
| author_sort | Bo Xiao |
| collection | DOAJ |
| description | Reliable and accurate photovoltaic (PV) output power projection is critical for power grid security, stability, and economic operation. However, because of the indirectness, unpredictability, and solar energy volatility, predicting precise and reliable photovoltaic output power is a complicated subject. The photovoltaic output power variable is evaluated in this study using a powerful machine learning approach called the support vector machine model based on gray-wolf optimization. A vast dataset of previously published papers was compiled for this purpose. Several studies were carried out to assess the suggested model. The statistical evaluation revealed that this model predicts absolute values with reasonable accuracy, including R2 and RMSE values of 0.908 and 74.6584, respectively. The practical input data were also subjected to sensitivity analysis. The results of this analysis showed that the air temperature parameter has a greater effect on the target parameter than the solar irradiance intensity parameter (relevancy factor equal to 0.75 compared to 0.49, respectively). The leverage approach was also used to test the accuracy of actual data, and the findings revealed that the vast majority of data is accurate. This basic but accurate model may be quite effective in predicting target values and could be a viable substitute for laboratory data. |
| format | Article |
| id | doaj-art-9c772191fe874f31b8a105b764c66906 |
| institution | Kabale University |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-9c772191fe874f31b8a105b764c669062025-02-03T06:06:49ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/3625541Gray-Related Support Vector Machine Optimization Strategy and Its Implementation in Forecasting Photovoltaic Output PowerBo Xiao0Hai Zhu1Sujun Zhang2Zi OuYang3Tandong Wang4Saeed Sarvazizi5School of Electronic and Electrical EngineeringSchool of Electronic and Electrical EngineeringMeteocontrol (Shanghai) Data Tech Co.Meteocontrol (Shanghai) Data Tech Co.School of Electronics and InformationDepartment of Petroleum EngineeringReliable and accurate photovoltaic (PV) output power projection is critical for power grid security, stability, and economic operation. However, because of the indirectness, unpredictability, and solar energy volatility, predicting precise and reliable photovoltaic output power is a complicated subject. The photovoltaic output power variable is evaluated in this study using a powerful machine learning approach called the support vector machine model based on gray-wolf optimization. A vast dataset of previously published papers was compiled for this purpose. Several studies were carried out to assess the suggested model. The statistical evaluation revealed that this model predicts absolute values with reasonable accuracy, including R2 and RMSE values of 0.908 and 74.6584, respectively. The practical input data were also subjected to sensitivity analysis. The results of this analysis showed that the air temperature parameter has a greater effect on the target parameter than the solar irradiance intensity parameter (relevancy factor equal to 0.75 compared to 0.49, respectively). The leverage approach was also used to test the accuracy of actual data, and the findings revealed that the vast majority of data is accurate. This basic but accurate model may be quite effective in predicting target values and could be a viable substitute for laboratory data.http://dx.doi.org/10.1155/2022/3625541 |
| spellingShingle | Bo Xiao Hai Zhu Sujun Zhang Zi OuYang Tandong Wang Saeed Sarvazizi Gray-Related Support Vector Machine Optimization Strategy and Its Implementation in Forecasting Photovoltaic Output Power International Journal of Photoenergy |
| title | Gray-Related Support Vector Machine Optimization Strategy and Its Implementation in Forecasting Photovoltaic Output Power |
| title_full | Gray-Related Support Vector Machine Optimization Strategy and Its Implementation in Forecasting Photovoltaic Output Power |
| title_fullStr | Gray-Related Support Vector Machine Optimization Strategy and Its Implementation in Forecasting Photovoltaic Output Power |
| title_full_unstemmed | Gray-Related Support Vector Machine Optimization Strategy and Its Implementation in Forecasting Photovoltaic Output Power |
| title_short | Gray-Related Support Vector Machine Optimization Strategy and Its Implementation in Forecasting Photovoltaic Output Power |
| title_sort | gray related support vector machine optimization strategy and its implementation in forecasting photovoltaic output power |
| url | http://dx.doi.org/10.1155/2022/3625541 |
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